The Big Data and Analytics Maturity Spectrum — Where Are You Now?

Many companies and organisations are on the drive to be data-driven given the extrinsic economic pressures from larger, data-driven companies. Governments around the world are also encouraging their respective public and civil service bodies to adopt a more data-driven culture, using data analytics to improve work productivity, improve services and accountability to stakeholders. Some have even gone as far as offering technology adoption grants and subsidies to the private industry in a bid to be more competitive in the current economy.

As such, you can argue that one is almost compelled to be data-driven.


Of course, I first have to define my term for data driven as there are varying definitions by industry thought leaders out there. I’m taking the very liberal approach to the term and not the commercialised definition. Being data-driven for me means exactly what the english definition of the word means — using data to drive business decisions. Hope we set that clear for now.


In a bid to be data-driven, many business and organisation leaders have issued orders for their middle-managers to charge forward on an ambitious plan and often finding a lack of success in their endeavours.

It seems almost too easy to craft out a strategy. “Let’s start collecting data!”. “Let’s craft out a dashboard to monitor key metrics!”. “We need to do data analytics!”. Data, platforms, dashboards, metrics and KPIs — buzzwords you’d hear being thrown around and the reality of the matter is, after the smoke fades away, people on the ground are still genuinely clueless on the way forward.

The truth is, many companies and organisations aspire to be data-driven. It is akin to a stage of enlightenment where people imagine decisions being guided by artificial intelligence, being able to predict what would happen before it happens and we never can make mistakes. There are many proponents to this dream and there are those who can say with belief that it is all undeniably possible.

Like all aspirations, we start with a big dream and then work our asses to be able to live that dream.

So you have that dream. You want your business or organisation to one day be data-driven. The question now is, where does your organisation, or you currently sitting on the Big Data and Analytics Maturity Model?

Every organisation sits somewhere along the spectrum and to be truly a data-driven organisation, one needs to be aware and honest as to the position you are at right now and the place you wish to be in the near-future.


So.. Where Do You Stand?

I. The Digital Infancy Stage — Ad-Hoc

At this stage past being resistant, organisations are aware of the potential of using data to analyse their business or operations. They are able to identify key metrics of success and implement manual collection/ collation of the identified data. Data is visualised in tables and simple charts, but at that level where the readings and findings can easily be confirmed through staff on-the-ground. Not too insightful but useful enough for monitoring and describe trends.

These data are often used as part of routine or regulatory reporting process and stored in lossy format. There are no data governance policies in place yet and little to no digitised processes.

*Note that I do not use the term management simply because there are many cases where those leading the charge towards being data-driven are not holding managerial positions.

II. The Digital Child Stage —Foundational

Organisations at this stage understand the value of using data at the tactical level. They actively track and monitor data that they collect to manage operational effectiveness. Organisations at this stage have started digitising some parts of their processes and autonomously collecting some or parts of the data they require either by use of applications or software. Data analytics used is basic; explaining and informing of events happening in the organisations.

At this stage, there are also clear defined roles in the form of key appointment holders to ensure identified KPIs are being met. These key appointment holders may be utilising some form of dashboards to track those metrics though they may still be manually collating the reports at the scheduled times.

Data collection is disparate and governance managed by each department leader or key appointment holder.

III. The Digital Youth Stage — Competitive

Leaders of organisations at the mature stage understand the merits of data analytics and is motivated to analyse internal business process data to uncover deeper insights to improve operational processes and client engagement. At this stage, organisations will also be able to make some projections to aid in forward-planning and resource allocation.

They invest a portion of their cost of operations to provision for and support the maintenance of the necessary infrastructures, hardware and manpower required for the initiative.

There is clear data management and governance policies aimed at unifying and integrating disparate sources of data, to ensure security and quality of the data collected. Often, there is a dedicated team in charge of data management and storage.

IV. The Digital Mature Stage — Differentiating

Organisations at the mature stage is able to utilise insights and use it as a competitive differentiator. At this stage, the organisation is able to utilise some form of analytics to enable data-driven actions that would maximise business value.

The organisation is able to utilise a wide breadth of data to be able to make holistic decisions based on customer and stakeholders insights, some form of data based on competitor analysis and match them with insights from its’ internal business processes.

At this stage, the organisation is comfortable working with data. Data governance is formalised and there is a significant number of employees in various units of the organisation who actively explores the use of data to uncover data stories and use-cases.

V. The Digital Advanced Stage — Breakaway

At this highly advanced stage, the organisation as a whole is aligned and have implemented data analytics into their day-to-day operations. Operations and work processes are highly digitised and automated. The use of applications and softwares to stream data, automate processes and generate insight is pervasive throughout the organisations. Data from the different units are highly connected and are in open communication to each other.

Data is being used to continuously drive business model innovation and information and the organisation is structured to adapt to improve using analytical insights.

The organisation is actively using models and algorithms to predict trends, make projections and is able to take actions to positively gain from the insights. At this level, information governance is integrated into all aspect of the organisation and they are poised to address their customers and stakeholders needs timely instead of reacting to them.

The value of implementing data analytics has been realised and is returning positive ROI.


So, where are you or your organisation at now in your digital journey? What can you do to progress ahead in the spectrum?

For most, the first step is to get an initial assessment of the availability of data that your organisation currently holds, be it locked in some data warehouse, spread across different departments or none at all. This is key in the first step of enablement as it will make a difference in your starting point.

The next step is to lay out a strategy of digitisation. It’s crucial to ensure that data remains liquid; that it is able to be extracted, transformed and shared across different departments to be used to generate different data stories. Liquid data is essentially ensuring that data is digitised and kept in a form that allows it to be used with versatility. Compare this to data that has been archived and saved in PDF format. We’ve had some of our clients who were only able to muster up PDF formats of historical data and it is definitely a steep point to begin your journey to data enlightenment.

A good strategy would also involve realistic adoption plans. Do not expect to be able to transform within a night or even a month. Being a data-driven organisation also entails a change in organisational culture. Members of the staff need to be aligned with the plans for the organisation to be data-driven and they will need to change some, or a large part of their current work process to fit the organisations’ goals.

Start with the lower-hanging fruits, things that do not impact greatly on the current workflow and build off that small win. Those small digitisation wins will yield confidence within the organisation and soon seep through the larger entity, leading to the ultimate aim of being data-driven.


The Big Data and Analytics Maturity Model is adapted from the 2014 IBM article http://www.ibmbigdatahub.com/blog/big-data-analytics-maturity-model It has been simplified specifically for this article for ease of understanding.